The world is demanding more from wireless communication technologies than ever before as more people around the world are subscribing to wireless communication. The Third-Generation (3G) wireless data services and applications, such as wireless email, web, digital picture taking/sending, assisted-GPS position location applications, video and audio streaming and TV broadcasting, are providing much more functionality than existed just a few years ago.
Code Division Multiple Access (CDMA) consistently provides better capacity for voice and data communications than other commercial mobile technologies, allowing more subscribers to connect at any given time. CDMA is a “spread spectrum” technology, allowing many users to occupy the same time and frequency allocations in a given band/space. As its name implies, Code Division Multiple Access assigns unique codes called spreading codes to each communication to differentiate it from others in the same spectrum. By the use of spreading codes, the frequency band of a transmission is spread to a chip rate which is larger than the actual data or information symbol rate. For example, if the used spreading code has the length of eight, eight symbols (referred to as “chips”) are transmitted for every data symbol.
The spreading codes have the property of orthogonality, meaning in mathematical terms that the inner product or correlation of the spreading codes used for communication is zero. Orthogonality of the spreading codes guarantees that transmission of a signal or sequence of data symbols respectively which is coded by a spreading code neither creates or propagates side effects to other signals coded by other orthogonal spreading codes and corresponding to other users of a communication system. A receiver looking for a certain spreading code of a certain transmitter will take signals coded by orthogonal spreading codes as a noise of the radio frequency (RF) channel. Since spreading codes can have different length, the property of orthogonality must be given also for spreading codes of different lengths. Construction of a spreading code can be achieved by use of an orthogonal variable spreading factor (OVSF) tree as shown in FIG. 1, wherein the abbreviation SF stands for the spreading factor characterising the length of the spreading code. The spreading factor may also be expressed by a definition “chip rate”/“data symbol rate” or by “data symbol duration”/“chip duration”.
Direct sequence code division multiple access (DS-CDMA) enables the users to share the same RF channel to transmit data simultaneously. The stream of information to be transmitted is divided into small pieces, each of which is allocated to a RF channel across the spectrum. The DS-CDMA transmitter multiplies each signal of a user by a distinct code waveform. The detector receives a signal composed of the sum of all signals of all users, which overlap in time and frequency. In a conventional DS-CDMA system, a particular signal of a user is detected by correlating the entire received signal with the code waveform of the user.
Spreading codes of different users in DS-CDMA fall into possibly different levels in an OVSF tree thus providing various levels of quality of service (QoS). UMTS FDD (Universal Mobile Telecommunication System Frequency Division Duplex) downlink is a particular example where user symbols are spread by spreading factors ranging from 4 to 512. In this and other similar systems, although the transmitted user signals at the base station (BS) side are orthogonal, this orthogonality no more exists at the mobile station (MS) front-end due to the multipath effect of the propagation channel between the transmitter and the receiver where the channel consists of more than one distinct propagation path for each signal of a user. Thus, multipath is a propagation phenomenon resulting in radio signals reaching the receiving antenna by two or more paths and provides at the same time the possibility of using signals that arrive in the receivers with different time delays. In CDMA the multipath signals are combined to make an even stronger signal at the receivers. Causes of multipath include atmospheric ducting, ionospheric reflection and refraction, and reflection from terrestrial objects, such as mountains and buildings.
However, the receivers have to be able to cope with the negative effect of multipath. For this reason, there are three common approaches to circumvent the problem of the loss of orthogonality which results in interference:                1. The most straightforward and basic approach is to treat the generated interference due to multipath as an additive white gaussian noise (AWGN) and implement the conventional Rake receiver to detect symbols of a user independently from others by collecting the energy from a number of delayed forms of the received signal via correlations with the spreading code of that particular user.        2. The second approach is interference suppression, which partially brings back orthogonality via usage of chip rate channel equalisers and again estimates the symbols of a particular user independently from others via correlating with its spreading code.        3. The third approach is interference cancellation (IC). Firstly, the symbols of known active interfering spreading codes are estimated via methods encompassing one of the first two approaches. Then the estimated symbols are respread, rechannelled and deleted from the originally received signal.        
For multipath environments the complexity of a receiver increases by a factor of N when N channels have to be supported.
Regarding the first approach, Rake receivers can handle only a finite number of paths of a multipath environment. Additionally, in case of M paths a Rake receiver will use M complex data correlators to correlate a received signal with the spreading code of a particular user, which is a complex and, concerning the required costs and power, an expensive approach.
Concerning the second approach, the interference is only suppressed and the required orthogonality is given only partially.
The present invention relates to the third approach where for the interference canceller it is essential to know the actual spreading codes and their powers. However, methods of the prior art representing said third approach like US20020057730-A1 and U.S. Pat. No. 6,678,314-B2 are still computationally complex and generate or cause processing delays. In order to enable fast data processing and transmission there is a continuing need for a substantially more efficient interference cancellation providing improved runtime.
In the prior art representing said third approach disclosed in US20020057730-A1 and U.S. Pat. No. 6,678,314-B2 the problem of determining the spreading sequences have been addressed. Their common approach is to detect the existence of codes by visiting several levels in the OVSF tree, doing multiple correlations at those levels and deciding for the existence of codes corresponding to the maximum correlation values. However, methods represented in both prior art documents are still computationally complex.
Moreover, in radio communications channel estimation is one of the major problems, particularly when the mobile system is subject to multipath fading, that is the transmission channel consists of more than one distinct propagation path for each signal of a user. When the CDMA technique is used to allow multiple users access to a single channel, the system is susceptible to the near-far effect. The near-far problem arises when the signals from the different users arrive at the receiver with widely varying power levels. The near-far problem has been shown to severely degrade the performance of standard single-user techniques (e.g., matched filters, correlators, etc.) in conventional CDMA systems. These systems try to limit the near-far problem with power control. However, even a small amount of the near-far effect can drastically degrade the performance of conventional receivers.
Properly estimating the spreading codes and deleting their contribution in the received signal is also beneficial for channel estimation since channel estimation quality based on correlating a known training pattern with the received noisy common pilot channel (CPICH) is also dramatically influenced by the multi-user interference.